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Bioinformatics Advance Access originally published online on November 25, 2004
Bioinformatics 2005 21(8):1502-1508; doi:10.1093/bioinformatics/bti162
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© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Sample size for gene expression microarray experiments{dagger}

Chen-An Tsai 1, Sue-Jane Wang 2, Dung-Tsa Chen 3 and James J. Chen 1,*

1Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration Jefferson, AR 72079, USA
2Division of Biometrics II, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration Rockville, MD 20857, USA
3Biostatistics and Bioinformatics Unit, University of Alabama at Birmingham 153 Wallace Tumor Institute, Birmingham, AL 35294, USA

*To whom correspondence should be addressed.

Motivation: Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true discovery and accuracy rates. The sample size problem is formulated as: the number of arrays needed in order to achieve the desired fraction of the specified measure at the desired family-wise power at the given type I error and (standardized) effect size.

Results: We present a general approach for estimating sample size under independent and equally correlated models using binomial and beta-binomial models, respectively. The sample sizes needed for a two-sample z-test are computed; the computed theoretical numbers agree well with the Monte Carlo simulation results. But, under more general correlation structures, the beta-binomial model can underestimate the needed samples by about 1–5 arrays.

Contact: jchen{at}nctr.fda.gov


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